Scoring protein relationships in functional interaction networks predicted from sequence data

 

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dc.contributor.author Mazandu, Gaston K en_ZA
dc.contributor.author Mulder, Nicola J en_ZA
dc.date.accessioned 2015-12-28T06:47:38Z
dc.date.available 2015-12-28T06:47:38Z
dc.date.issued 2011 en_ZA
dc.identifier.citation Mazandu, G. K., & Mulder, N. J. (2011). Scoring protein relationships in functional interaction networks predicted from sequence data. PLoS One, 6(4), e18607. doi:10.1371/journal.pone.0018607 en_ZA
dc.identifier.uri http://hdl.handle.net/11427/16040
dc.identifier.uri http://dx.doi.org/10.1371/journal.pone.0018607
dc.description.abstract The abundance of diverse biological data from various sources constitutes a rich source of knowledge, which has the power to advance our understanding of organisms. This requires computational methods in order to integrate and exploit these data effectively and elucidate local and genome wide functional connections between protein pairs, thus enabling functional inferences for uncharacterized proteins. These biological data are primarily in the form of sequences, which determine functions, although functional properties of a protein can often be predicted from just the domains it contains. Thus, protein sequences and domains can be used to predict protein pair-wise functional relationships, and thus contribute to the function prediction process of uncharacterized proteins in order to ensure that knowledge is gained from sequencing efforts. In this work, we introduce information-theoretic based approaches to score protein-protein functional interaction pairs predicted from protein sequence similarity and conserved protein signature matches. The proposed schemes are effective for data-driven scoring of connections between protein pairs. We applied these schemes to the Mycobacterium tuberculosis proteome to produce a homology-based functional network of the organism with a high confidence and coverage. We use the network for predicting functions of uncharacterised proteins. Availability Protein pair-wise functional relationship scores for Mycobacterium tuberculosis strain CDC1551 sequence data and python scripts to compute these scores are available at http://web.cbio.uct.ac.za/~gmazandu/scoringschemes . en_ZA
dc.language.iso eng en_ZA
dc.publisher Public Library of Science en_ZA
dc.rights This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. en_ZA
dc.rights.uri http://creativecommons.org/licenses/by/4.0 en_ZA
dc.source PLoS One en_ZA
dc.source.uri http://journals.plos.org/plosone en_ZA
dc.subject.other Protein domains en_ZA
dc.subject.other Protein interaction networks en_ZA
dc.subject.other Sequence databases en_ZA
dc.subject.other Sequence alignment en_ZA
dc.subject.other Mycobacterium tuberculosis en_ZA
dc.subject.other Sequence similarity searching en_ZA
dc.subject.other Protein interactions en_ZA
dc.subject.other Lipid metabolism en_ZA
dc.title Scoring protein relationships in functional interaction networks predicted from sequence data en_ZA
dc.type Journal Article en_ZA
dc.rights.holder © 2011 Mazandu, Mulder en_ZA
uct.type.publication Research en_ZA
uct.type.resource Article en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Health Sciences en_ZA
dc.publisher.department Institute of Infectious Disease and Molecular Medicine en_ZA
uct.type.filetype Text
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This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Except where otherwise noted, this item's license is described as This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.